Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
8 results
Search Results
Item Multi-spectral satellite image classification using Glowworm Swarm Optimization(2011) Senthilnath, J.; Omkar, S.N.; Mani, V.; Tejovanth, N.; Diwakar, P.G.; Shenoy B, A.This paper investigates a new Glowworm Swarm Optimization (GSO) clustering algorithm for hierarchical splitting and merging of automatic multi-spectral satellite image classification (land cover mapping problem). Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to classify all the basic land cover classes of an urban region in a satisfactory manner. In unsupervised classification methods, the automatic generation of clusters to classify a huge database is not exploited to their full potential. The proposed methodology searches for the best possible number of clusters and its center using Glowworm Swarm Optimization (GSO). Using these clusters, we classify by merging based on parametric method (k-means technique). The performance of the proposed unsupervised classification technique is evaluated for Landsat 7 thematic mapper image. Results are evaluated in terms of the classification efficiency - individual, average and overall. © 2011 IEEE.Item Multi-objective Genetic Algorithm for efficient point matching in multi-sensor satellite image(2012) Senthilnath, J.; Omkar, S.N.; Mani, V.; Kalro, N.P.; Diwakar, P.G.This paper investigates a new approach for point matching in multi-sensor satellite images. The feature points are matched using multi-objective optimization (angle criterion and distance condition) based on Genetic Algorithm (GA). This optimization process is more efficient as it considers both the angle criterion and distance condition to incorporate multi-objective switching in the fitness function. This optimization process helps in matching three corresponding corner points detected in the reference and sensed image and thereby using the affine transformation, the sensed image is aligned with the reference image. From the results obtained, the performance of the image registration is evaluated and it is concluded that the proposed approach is efficient. © 2012 IEEE.Item Multi-sensor satellite image analysis using niche genetic algorithm for flood assessment(2012) Senthilnath, J.; Shreyas, P.B.; Rajendra, R.; Omkar, S.N.; Mani, V.; Diwakar, P.G.In this paper, cluster splitting and merging algorithms are used for flood assessment using LISS-III (before flood) and SAR (during flood) images. Bayesian Information Criteria (BIC) is used to determine the optimal number of clusters. Keeping this constraint, the cluster centers are generated using the cluster splitting techniques, namely Mean Shift Clustering (MSC), and Niche Genetic Algorithm (NGA). The merging method is used to group the data points into their respective classes, using the cluster centers obtained from the above techniques. These techniques are applied on the LISS-III and SAR image. Further, the resultant images are overlaid to analyze the extent of the flood in individual land classes. A performance comparison of these techniques (MSC and NGA) is presented. From the results obtained, we deduce that the NGA is efficient. © 2012 Springer-Verlag.Item Dynamic land use and coastline changes in active estuarine regions - A study of sundarban delta(International Society for Photogrammetry and Remote Sensing, 2014) Thomas, J.V.; Arunachalam, A.; Jaiswal, R.; Diwakar, P.G.; Kiran, B.Alteration of natural environment in the wake of global warming is one of the most serious issues, which is being discussed across the world. Over the last 100 years, global sea level rose by 1.0-2.5 mm/y. Present estimates of future sea-level rise induced by climate change range from 28 to 98 cm for the year 2100. It has been estimated that a 1-m rise in sea-level could displace nearly 7 million people from their homes in India. The climate change and associated sea level rise is proclaimed to be a serious threat especially to the low lying coastal areas. Thus, study of long term effects on an estuarine region not only gives opportunity for identifying the vulnerable areas but also gives a clue to the periods where the sea level rise was significant and verifies climate change impact on sea level rise. Multi-temporal remote sensing data and GIS tools are often used to study the pattern of erosion/ accretion in an area and to predict the future coast lines. The present study has been carried out in the Indian Sundarbans area. Major land cover/ land use classes has been delineated and change analysis of the land cover/ land use feature was performed using multi-temporal satellite images (Landsat MSS, TM, ETM+) from 1973 to 2010. Multivariate GIS based analysis was carried out to depict vulnerability and its trend, spatially. Digital Shoreline change analysis also was attempted for two islands, namely, Ghoramara and Sagar Islands using the past 40 years of satellite data and validated with 2012 Resourcesat-2 LISS III data.Item Development of polarimetric decomposition techniques for land use and land cover mapping using RISAT-1 radar satellite sensor data(Asian Association on Remote Sensing Sh1939murai@nifty.com, 2015) Sridhar, J.; Mahadev, S.; Vanjare, A.; Omkar, S.N.; Diwakar, P.G.In this paper, we are examines polarimetric decomposition techniques like on Pauli decomposition and Sphere Di-Plane Helix (SDH) decomposition of RISAT-1 satellite image for land use and land cover mapping. The data processing methods adopted are 1) Pre-processing, antenna pattern correction, sigma nought calibration, Speckle Reduction, 2) Polarimetric Decomposition and 3) Polarimetric Classification. We have used RISAT-1 satellite image datasets of Mysore-Mandya region of Karnataka, India for classifying five classes - agricultural lands, urban area, forest land, water land and barren land. Polarimetric SAR data possess a high potential because it captures earth land surface features. After applying the polarimetric classification techniques, post-classification techniques is applied in order to access the classification accuracy. The Post-classification step gives the over-all accuracy was observed to be higher in the SDH decomposed image, as it operates on individual pixels on a coherent basis and utilises the complete intrinsic coherent nature of polarimetric SAR data as compared to the ground truth collected through GPS measurements and maps. Thereby, making SDH decomposition particularly suited for analysis of high-resolution SAR data. The Pauli Decomposition represents all the polarimetric information in a single SAR image however interpretation of the resulting image in less accuracy. The SDH decomposition technique seems to produce better results and interpretation as compared to Pauli Decomposition however more quantification and further analysis are being done in this area of research. The comparison of the Polarimetric decomposition techniques will be the scope of the paper. Polarimetric decomposition techniques helps in better understanding earth land surface features.Item Hierarchical clustering algorithm for land cover mapping using satellite images(2012) Senthilnath, J.; Omkar, S.N.; Mani, V.; Tejovanth, N.; Diwakar, P.G.; Archana Shenoy, B.This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust. © 2012 IEEE.Item Integration of speckle de-noising and image segmentation using Synthetic Aperture Radar image for flood extent extraction(Indian Academy of Sciences, 2013) Senthilnath, J.; Handiru, H.V.; Rajendra, R.; Omkar, S.N.; Mani, V.; Diwakar, P.G.Flood is one of the detrimental hydro-meteorological threats to mankind. This compels very efficient flood assessment models. In this paper, we propose remote sensing based flood assessment using Synthetic Aperture Radar (SAR) image because of its imperviousness to unfavourable weather conditions. However, they suffer from the speckle noise. Hence, the processing of SAR image is applied in two stages: speckle removal filters and image segmentation methods for flood mapping. The speckle noise has been reduced with the help of Lee, Frost and Gamma MAP filters. A performance comparison of these speckle removal filters is presented. From the results obtained, we deduce that the Gamma MAP is reliable. The selected Gamma MAP filtered image is segmented using Gray Level Co-occurrence Matrix (GLCM) and Mean Shift Segmentation (MSS). The GLCM is a texture analysis method that separates the image pixels into water and non-water groups based on their spectral feature whereas MSS is a gradient ascent method, here segmentation is carried out using spectral and spatial information. As test case, Kosi river flood is considered in our study. From the segmentation result of both these methods are comprehensively analysed and concluded that the MSS is efficient for flood mapping. © Indian Academy of Sciences.Item RSCDNet: A Robust Deep Learning Architecture for Change Detection From Bi-Temporal High Resolution Remote Sensing Images(Institute of Electrical and Electronics Engineers Inc., 2023) Deepanshi; Barkur, R.; Suresh, D.; Lal, S.; Chintala, C.S.; Diwakar, P.G.Accurate change detection from high-resolution satellite and aerial images is of great significance in remote sensing for precise comprehension of Land cover (LC) variations. The current methods compromise with the spatial context; hence, they fail to detect and delineate small change areas and are unable to capture the difference between features of the bi-temporal images. This paper proposes Remote Sensing Change Detection Network (RSCDNet) - a robust end-to-end deep learning architecture for pixel-wise change detection from bi-temporal high-resolution remote-sensing (HRRS) images. The proposed RSCDNet model is based on an encoder-decoder framework integrated with the Modified Self-Attention (MSA) andthe Gated Linear Atrous Spatial Pyramid Pooling (GL-ASPP) blocks; both efficient mechanisms to regulate the field-of-view while finding the most suitable trade-off between accurate localization and context assimilation. The paper documents the design and development of the proposed RSCDNet model and compares its qualitative and quantitative results with state-of-the-art HRRS change detection architectures. The above mentioned novelties in the proposed architecture resulted in an F1-score of 98%, 98%, 88%, and 75% on the four publicly available HRRS datasets namely, Staza-Tisadob, Onera, CD-LEVIR, and WHU. In addition to the improvement in the performance metrics, the strategic connections in the proposed GL-ASPP and MSA units significantly reduce the prediction time per image (PTPI) and provide robustness against perturbations. Experimental results yield that the proposed RSCDNet model outperforms the most recent change detection benchmark models on all four HRRS datasets. © 2017 IEEE.